Increasing student science, technology, engineering and mathematics engagement through phyphox activities: Three practical examples
Bibliographic record
Abstract
Abstract For decades, educators in science, technology, engineering and mathematics (STEM) have strived to break the vicious circle of student disengagement at both secondary and post‐secondary levels. Despite the widespread availability of technologies like smartphones, STEM pedagogies have largely remained unchanged. Too often, students learn STEM theoretically with little hands‐on experience or opportunities to engage in authentic, research‐like activities. Modern smartphones can offer unprecedented opportunities for active STEM learning, but can also serve as distractors. Therefore, it is essential for teachers to acquire the pedagogical knowledge to harness these powerful tools effectively. This paper explores the potential of integrating smartphones into physics labs to enrich STEM learning. By leveraging smartphones' advanced capabilities for experimental design, data collection, and analysis, we have implemented a smartphone‐enhanced pedagogical approach in secondary physics classes and province‐wide Physics Olympics. We also implemented smartphone‐enhanced STEM pedagogies in teacher education. Our initial pilot study has yielded promising outcomes: enhanced student engagement in physics and deeper conceptual understanding. To advance this initiative, we propose structured teacher mentorship and professional development, empowering STEM educators to seamlessly integrate smartphones into their teaching. By embracing these modern educational tools, adopting evidence‐based pedagogical approaches, and supporting future and practicing educators we can make STEM learning more engaging and relevant for all students.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".